• ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance 

      Fan, Ningning; Bauer, Christoph; Stork, Conrad; de Bruyn Kops, Christina; Kirchmair, Johannes (Peer reviewed; Journal article, 2019-10)
      Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ...
    • Convolutional Neural Networks for Malaria Detection 

      Gimse, Håkon (Master thesis, 2019-12-13)
      Together with doctors at Haukeland University Hospital in Bergen, we wanted to research how the diagnosis of malaria can be improved. We propose a method that can detect malaria parasites (Plasmodium falciparum) in microscope ...
    • Employing Deep Learning for Fish Recognition 

      Reithaug, Adrian (Master thesis, 2018-08-21)
      Underwater imagery processing is in high demand, but the unrestricted environment makes it difficult to develop methods for analyzing it. Not only is obtaining a dataset for a single species difficult, but there are reported ...
    • Machine learning applications in proteomics research: How the past can boost the future 

      Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, S; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart (Peer reviewed; Journal article, 2014)
      Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution ...
    • A novel approach to computing super observations for probabilistic wave model validation 

      Bohlinger, Patrik; Breivik, Øyvind; Economou, Theodoros; Müller, Malte (Peer reviewed; Journal article, 2019-07)
      In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along track is widely ...
    • An overview of deep learning in medical imaging focusing on MRI 

      Lundervold, Alexander Selvikvåg; Lundervold, Arvid (Peer reviewed; Journal article, 2018-12-13)
      What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started ...